Data mining is the automatic process of
sorting through large amounts of data and discovering patterns. It has been described as "the nontrivial extraction of implicit, previously unknown, and potentially useful information from data" (
Frawley et al 1992). For an introduction to data mining, see presentation slides on
the website of Professor Robert Stine, from whom I learned the topic at
ICPSR.
Spatial data ming is the application of data mining techniques to
spatial data, with the objective to discover spatial patterns. Still at the early stage of development, spatial data mining is considered a new field with boundaries yet to be defined. In general, spatial data mining focuses more on large data sets and tend to be exploratory in nature, with heavy reliance on newly developed computational powers (
Professor Guo).
In a recent project I am using
exploratory spatial data analysis (EDA) to visualize and analyze spatial patterns of inter-local fiscal relations with a comprehensive database that include hundreds of fiscal variables for all local governments in Georgia. This may be an example of spatial data mining in the study of public finance and local government management. I am eager to meet others with similar interests. (Click
here to get my presentation slides on
ABFM 2007.)
Some resources about spatial data mining:
- Spatial data mining research group in UMN
- Spatial data mining @ University of Munich
